Tran Ulrich S, Lallai Taric, Gyimesi Marton, Baliko Josef, Ramazanova Dariga, Voracek Martin
Department of Cognition, Emotion, and Methods in Psychology, School of Psychology, University of Vienna, Vienna, Austria.
Front Psychol. 2021 Aug 20;12:716164. doi: 10.3389/fpsyg.2021.716164. eCollection 2021.
Although distributional inequality and concentration are important statistical concepts in many research fields (including economics, political and social science, information theory, and biology and ecology), they rarely are considered in psychological science. This practical primer familiarizes with the concepts of statistical inequality and concentration and presents an overview of more than a dozen useful, popular measures of inequality (including the Gini, Hoover, Rosenbluth, Herfindahl-Hirschman, Simpson, Shannon, generalized entropy, and Atkinson indices, and tail ratios). Additionally, an interactive web application (R Shiny) for calculating and visualizing these measures, with downloadable output, is described. This companion Shiny app provides brief introductory vignettes to this suite of measures, along with easy-to-understand user guidance. The Shiny app can readily be used as an intuitively accessible, interactive learning and demonstration environment for teaching and exploring these methods. We provide various examples for the application of measures of inequality and concentration in psychological science and discuss venues for further development.
尽管分布不平等和集中度是许多研究领域(包括经济学、政治和社会科学、信息论以及生物学和生态学)中的重要统计概念,但它们在心理学领域却很少被考虑。这本实用入门指南将让读者熟悉统计不平等和集中度的概念,并概述十几种有用且常用的不平等度量方法(包括基尼系数、胡佛指数、罗森布鲁斯指数、赫芬达尔-赫希曼指数、辛普森指数、香农指数、广义熵指数、阿特金森指数以及尾部比率)。此外,还介绍了一个用于计算和可视化这些度量指标并可下载输出结果的交互式网络应用程序(R Shiny)。这个配套的Shiny应用程序提供了关于这一系列度量指标的简要介绍性小短文,以及易于理解的用户指南。该Shiny应用程序可以很容易地用作一个直观易用的交互式学习和演示环境,用于教授和探索这些方法。我们提供了不平等和集中度度量指标在心理学领域应用的各种示例,并讨论了进一步发展的方向。